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Bayesian Learning with Local Support Vector Machines for Cancer Classification with Gene Expression Data

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Applications of Evolutionary Computing (EvoWorkshops 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3449))

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Abstract

This paper describes a novel method for improving classification of support vector machines (SVM) with recursive feature selection (SVM-RFE) when applied to cancer classification with gene expression data. The method employs pairs of support vectors of a linear SVM-RFE classifier for generating a sequence of new SVM classifiers, called local support classifiers. This sequence is used in two Bayesian learning techniques: as ensemble of classifiers in Optimal Bayes, and as attributes in Naive Bayes. The resulting classifiers are applied to four publically available gene expression datasets from leukemia, ovarian, lymphoma, and colon cancer data, respectively. The results indicate that the proposed approach improves significantly the predictive performance of the baseline SVM classifier, its stability and robustness, with satisfactory results on all datasets. In particular, perfect classification is achieved on the leukemia and ovarian cancer datasets.

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Author information

Authors and Affiliations

  1. Department of Computer Science, Vrije Universiteit Amsterdam, The Netherlands

    Elena Marchiori

  2. Laboratoire de Recherche en Informatique, CNRS-INRIA, Université Paris-Sud Orsay, France

    Michèle Sebag

Authors
  1. Elena Marchiori
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  2. Michèle Sebag
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Editor information

Editors and Affiliations

  1. Johannes Gutenberg University, Mainz, Germany

    Franz Rothlauf

  2. Institute AIFB, University of Karlsruhe, 76128, Karlsruhe, Germany

    Jürgen Branke

  3. Dipartimento di Ingegneria dell’Informazione, Università di Parma,  

    Stefano Cagnoni

  4. School of Mathematical and Computer Science, Heriot-Watt University, EH14 8AS, Edinburgh, UK

    David Wolfe Corne

  5. Institute of Computer Science, University of Bremen, 28359, Bremen, Germany

    Rolf Drechsler

  6. Honda Research Institute Europe GmbH, Offenbach/Main, Germany

    Yaochu Jin

  7. CISUC, Department of Informatics Engineering, University of Coimbra, Polo II of the University of Coimbra, 3030, Coimbra, Portugal

    Penousal Machado

  8. ICIS, Radboud University Nijmegen, The Netherlands

    Elena Marchiori

  9. Universidade de A Coruña, CP 15071, A Coruña, Spain

    Juan Romero

  10. School of Computing Sciences, University of East Anglia, UEA Norwich, NR4 7TJ, Norwich, UK

    George D. Smith

  11. Dipartimento di Automatica e Informatica, Politecnico di Torino, Italy

    Giovanni Squillero

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© 2005 Springer-Verlag Berlin Heidelberg

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Marchiori, E., Sebag, M. (2005). Bayesian Learning with Local Support Vector Machines for Cancer Classification with Gene Expression Data. In: Rothlauf, F., et al. Applications of Evolutionary Computing. EvoWorkshops 2005. Lecture Notes in Computer Science, vol 3449. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-32003-6_8

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  • DOI: https://doi.org/10.1007/978-3-540-32003-6_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-25396-9

  • Online ISBN: 978-3-540-32003-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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